English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper we introduce Smooth Particle Networks (SPNets), a framework forintegrating fluid dynamics with deep networks. SPNets adds two new layers tothe neural network toolbox: ConvSP and ConvSDF, which enable computing physicalinteractions with unordered particle sets. We use these lay- ers in combinationwith standard neural network layers to directly implement fluid dynamics insidea deep network, where the parameters of the network are the fluid parametersthemselves (e.g., viscosity, cohesion, etc.). Because SPNets are imple- mentedas a neural network, the resulting fluid dynamics are fully differentiable. Wethen show how this can be successfully used to learn fluid parameters fromdata, perform liquid control tasks, and learn policies to manipulate liquids.

SPNets: Differentiable Fluid Dynamics for Deep Neural Networks